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Breathing motion complicates many image-guided interventions working on the thorax or upper abdomen. However, prior knowledge provided by a statistical breathing model, can reduce the uncertainties of organ location. In this paper, a prediction framework for statistical motion modeling is presented and different representations of the dynamic data for motion model building of the lungs are investigated. Evaluation carried out on 4D-CT data sets of 10 patients showed that a displacement vector-based representation can reduce most of the respiratory motion with a prediction error of about 2 mm, when assuming the diaphragm motion to be known.

Temporal subtraction techniques using 2D image registration improve the detectability of interval changes from chest radiographs. Although such methods are well known for some time they are not widely used in radiologic practice. The reason are strong pose differences between these follow-up acquisitions with a time interval of months to years in between. Such strong perspective differences occur in a reasonable number of cases. They cannot be compensated by available image registration methods and thus mask interval changes to be undetectable. A method is proposed to estimate a 3D pose difference by the adaptation of a 3D rib cage model to both projections. The difference between both is then compensated for, thus producing a subtraction image with virtually no change in pose. No 3D image data is used. The accuracy of pose estimation is validated with chest phantom images under controlled geometric conditions.

The delineation of anatomical structures in medical images can be achieved in an efficient and robust manner using statistical anatomical organ models, which has been demonstrated for an already considerable set of organs, including the heart. While it is possible to provide models with sufficient shape variability to cope, to a large extent, with inter-patient variability, as long as object topology is conserved, it is a fundamental problem to cope with topological organvariability. We address this by creating a set of model variants and selecting the most appropriate model variant for the patient at hand. We propose a hybrid method combining model-based image analysiswith a guided region growing approach for automated anatomical variant selection and apply it to the left atrium in cardiac CT images. Concerning the human heart, the left atrium is the most variable sub-structure with a variable number of pulmonary veins drainng into it.It is of large clinical interest in the context of atrial fibrillation and related interventions.